by Han, L and van Hemert, J
Abstract:
Wavelet transform has been proved to be a powerful tool for characterizing network traffic. However, the resulting decomposition of wavelet transform typically forms the high-dimension space. It is obviously problematic on compact representation, visualization, and modeling based on these high-dimensional data. In this study, we employ data projection techniques to represent the high dimensional wavelet decomposition of network traffic data in a low dimensional space to facilitate the visual analysis of network traffic pattern. A low-dimensional representation can significantly reduce the model complexity, and the features of traffic pattern can be presented with a small number of parameters. The experimental results show that the proposed method could effectively discriminate the different application flows, such as FTP and P2P data flows.
Reference:
A Novel Visual Discriminator for Network Traffic Patterns (Han, L and van Hemert, J), In Advanced Engineering Computing and Applications in Sciences, volume 2, 2008.
Bibtex Entry:
@article{HH2008,
_day = {01},
abstract = {Wavelet transform has been proved to be a powerful tool for characterizing network traffic. However, the resulting decomposition of wavelet transform typically forms the high-dimension space. It is obviously problematic on compact representation, visualization, and modeling based on these high-dimensional data. In this study, we employ data projection techniques to represent the high dimensional wavelet decomposition of network traffic data in a low dimensional space to facilitate the visual analysis of network traffic pattern. A low-dimensional representation can significantly reduce the model complexity, and the features of traffic pattern can be presented with a small number of parameters. The experimental results show that the proposed method could effectively discriminate the different application flows, such as FTP and P2P data flows. },
author = {Han, L and van Hemert, J},
date-added = {2008-10-10 14:26:54 +0100},
date-modified = {2009-06-29 12:09:38 +0100},
doi = {10.1109/ADVCOMP2008.35},
isbn = {978-0-7695-3369-8},
journal = {Advanced Engineering Computing and Applications in Sciences},
keywords = {data mining},
pages = {141--146},
title = {A Novel Visual Discriminator for Network Traffic Patterns},
volume = {2},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1109/ADVCOMP2008.35}}